Lea 3.4.0 is now released!
http://pypi.org/project/lea/3.4.0
What is Lea?
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Lea is a Python module aiming at working with discrete probability distributions in an intuitive way.
It allows you modeling a broad range of random phenomena: gambling, weather, finance, etc. More generally, Lea may be used for any finite set of discrete values having known probability: numbers, booleans, date/times, symbols,.
Each probability distribution is modeled as a plain object, which can be
named, displayed, queried or processed to produce new probability distributions.
Lea also provides advanced functions and Probabilistic Programming (PP) features; these include conditional probabilities, joint probability distributions, Bayesian networks, Markov chains and symbolic computation.
All probability calculations in Lea are performed by a new exact algorithm,
the Statues algorithm, which is based on variable binding and recursive generators. For problems intractable through exact methods, Lea provides on-demand approximate algorithms, namely MC rejection sampling and
likelihood weighting.
Beside the above-cited functions, Lea provides some machine learning
functions, including Maximum-Likelihood and Expectation-Maximization algorithms.
Lea can be used for AI, education (probability theory & PP), generation of random samples, etc.
LGPL - Python 2.6+ / Python 3 supported
For a 5 minutes tour. check out the poster presented at PROBPROG2020 conference:
http://probprog.cc/assets/posters/fri/69.pdf
What's new in Lea 3.4.0?
------------------------
Lea 3.4.0 includes two important improvements over 3.3.x:
1) Introduction of "evidence contexts", allowing to factorize conditions
when calculating conditional probabilities
http://bitbucket.org/piedenis/lea/wiki/Lea3_Tutorial_3#markdown-header-evide nce-contexts-evidence-add_evidence-methods
2) Optimize calculation for several queries, which were intractable in an
exact way with previous Lea versions
http://bitbucket.org/piedenis/lea/issues/60/scaling-exact-inference-optimiza tion
This version contains also a couple of improvements on
usability/consistency.
To learn more...
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Lea 3 on PyPI ->
http://pypi.org/project/lea
Lea project page ->
http://bitbucket.org/piedenis/lea
Documentation ->
http://bitbucket.org/piedenis/lea/wiki/Home
Statues algorithm ->
http://link.springer.com/chapter/10.1007/978-3-030-52246-9_10
With the hope that Lea can make this universe less erratic,
Pierre Denis
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